Abstract
Experimental tests were carried out to assess the fatigue strength of four types of welded joints, made of AH36 steel and used for ship structures. The joints differ for the presence of weld defects and for the thickness value. Fatigue tests were carried out applying axial cyclic loads at a frequency of 20 Hz and at a stress ratio R = 0.5. The temperature e increment of the specimen surface was detected during the load application by means of an infrared camera. The analysis of the thermographic images allowed the assessment of both the fatigue strength of the welded joints, applying the rapid thermographic method, and the S-N curve by the energy approach. Moreover, 3D computed tomography was used for the analysis of the defective welded joints.
Highlights
Introduction of WeldPorosity Defects on FatigueA metallic specimen, subjected to fatigue loading, changes its temperature and the specimen heating can be considered as a fatigue indicator
There are several studies in literature about the application of approaches for the fatigue life prediction, whereas the defect influence on fatigue strength has been studied in only few papers, which generally regard the fatigue in very high cycle regimes
This paper aims to assess the fatigue strength and life of AH36 butt welded joints in presence of defects, which were extracted from a real hull-plating
Summary
Introduction of WeldPorosity Defects on FatigueA metallic specimen, subjected to fatigue loading, changes its temperature and the specimen heating can be considered as a fatigue indicator. The thermographic method (TM) [13], based on the thermographic technique, allows for a reliable assessment of the fatigue strength and of the whole fatigue curve of materials, mechanical components and structural details by means of nonexcessive time-consuming tests. An approach to predict fatigue life of 6061 aluminum alloy based on a simplified defect model was proposed in [26] according to the following steps: firstly, a prediction model of the stress concentration factor, due to the defects, was evaluated through the support vector regression algorithm by using the defect data at different fatigue loading stages, obtained by computed tomography (CT)
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